Because spatial transformation based on deepth-based[1]}, [2]}, transformer-based[3]}, [4]} and other methods are computationally expensive and unfriendly to autopilot chips, our spatial transformation modelule is based on MLP[5]} which is called View Relation Module. View Relation Module use a view relation module \(R\)  to learn the relations between the any two pixel positions in flattened first-view feature map and flattened top-down-view feature map. However, View Relation Module did not perform well in previous work because camera's parameters can not incorporated into it. And View Relation Module is sensitive to the position of the front view feature layer. Therefore, we designed the Virtual Camera to unify the intrinsic and extrinsic parameters of the camera. Meanwhile, our work analyzes the effect of resolution of front-view features doing spatial transformation by View Relation Module. Low resolution features are found to be more suitable for spatial transformation by View Relation Module through experiments. We speculate that the low resolution features contain more global information. And since the MLP-based spatial transformation is a fixed mapping, the low-resolution features are easier to learn. Inspired by the DeepLab[6]}, we designed a Spatial Transformation Pyramid based on View Relation Module. We use 1/64 resolution feature of the input image s64 and 1/32 resolution feature of the input image s32 to do spatial transformation respectively, and then concatenate the results of both.
\({f_t}[i] = concate(R_i^{s32}({f^{s32}}[1], \ldots ,{f^{s32}}[H{W^{s32}}]),R_i^{s64}({f^{s64}}[1], \ldots ,{f^{s64}}[H{W^{s64}}]))\) 
